Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios

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1 sensors Article Basic Simulation Environment for Highly Cusmized Connected and Aunomous Vehicle Kematic Scenarios Lguo Chai 1, Baigen Cai 2, Wei ShangGuan 1, *, Jian Wang 1 and Huashen Wang 1 1 School Electronics and Information Engeerg, Beijg Jiaong University, NO. 3 Shangyuancun, Haidian, Beijg 144, Cha; lgchai@bjtu.edu.cn (L.C.); wangj@bjtu.edu.cn (J.W.); hswang@bjtu.edu.cn (H.W.) 2 School Computer and Information Technology, Beijg Jiaong University, Beijg 144, Cha; bgcai@bjtu.edu.cn * Correspondence: wshg@bjtu.edu.cn; Tel.: ; Fax: Received: 2 July 217; Accepted: 2 August 217; Publhed: 23 August 217 Abstract: To enhance reality Connected and Aunomous Vehicles (CAVs) kematic simulation scenarios and guarantee accuracy and reliability verification, a four-layer CAVs kematic simulation framework, which composed with road network layer, vehicle operatg layer, uncertaties lg layer and demonstratg layer, proposed th paper. Properties tersections are defed describe road network. A target position based vehicle position updatg method designed simulate such vehicle behaviors as lane changg and turng. Vehicle kematic s are implemented mata status vehicles when y are movg wards target position. Priorities for dividual vehicle control are authorized for different layers. Operation mechanms CAVs uncertaties, which are defed as position error and communication th paper, are implemented simulation enhance reality simulation. A simulation platform developed based on proposed methodology. A comparon simulated and oretical vehicle has been analyzed prove validity and creditability platform. The scenario rear-end collion avoidance conducted verify uncertaties operatg mechanms, and a slot-based tersections (SIs) control strategy realized and verified simulation platform show supports platform CAVs kematic simulation and verification. Keywords: connected and aunomous vehicles; road network description; vehicle kematic s; uncertaties lg; application verification; simulation platform 1. Introduction Traffic simulation stware widely used verifyg and optimizg traffic coordatg algorithms, due its charactertics repeatability, maneuverability, accuracy and low-cost. For purpose beg aimed at different requirements, different traffic simulation stware developed. CORSIM, SimTraffic, AIMSUN, VISSIM, PARAMICS, etc., are mostly used microscopic traffic simulation, while CORFLO, KRONOS, KWaves [1], etc., are used macroscopic traffic simulation and SPSS, STATISTICA [2,3] are mostly used vehicle dynamical analyzg. To better simulate different traffic scenarios, most stware supports application program terfaces (APIs) are for secondary developg, and researchers have obtaed achievements based on traffic simulation stware. As development sensor and communication technology improves, connected and aunomous vehicles (CAVs), connected vehicles (CVs) and aunomous vehicles (AVs) [4] are able better serve traffic. Due advantages simulation, many control strategies CAVs have been conducted simulation environment. Sensors 217, 17, 1938; doi:1.339/s

2 Sensors 217, 17, Gueriau [5] proposed a simulation framework by troducg cooperative traffic s based on Multi-Agent ory an open-source traffic simular. The resultg simulation framework robust and able assess potential benefits cooperative traffic control strategies different traffic configurations. Zha [6] proposed and evaluated a dilemma zone protection framework via vehicle frastructure communications, and examed effectiveness proposed framework a simulation test bed built VISSIM. Fredette [7] developed a simulated road network resolve dconnect between powertra engeerg and eco-drivg by presentg development a simplified methodology for ensurg that eco-drivg scenarios are contextualized agast traditional drive-cycle based text techniques. Goodall [8] proposed a novel technique estimate positions unconnected vehicles based on behaviors communicatg vehicles along a signalized arterial and simulated and verified method microscopically usg a commercial traffic simulation stware package. From analys above, we can conclude that s and control strategies verification can be grouped three categories: first category API based verification commercial traffic simulation stware, such as Paramics and Vsim; second category redevelopment beyond open source traffic simulation stware, such as MITSIMLab [9] and SUMO [1]; and third category establh a new fundamental simulation environment. The ma difference between CAVs simulation and traditional traffic simulation controllg objects. In traditional traffic simulation, parameters signal formation, channelization, lk properties, etc., are changed enforce vehicles change behaviors. In CAVs simulation, based on V2X communication and status perception, control strategies should be generated each dividual vehicle better simulate aunomy vehicle. Due operation mechanm traffic simulation stware, sometimes it very complicated for researchers implement cusmized s or control strategies with API constrats, or s and algorithms we implement stware are not able operate as we anticipate. In th situation, fundamental simulation environment should be built verify s and control strategies. Control strategies CAVs are usually generated based on status perception and communication. Radar [11], LIDAR, camera, GPS, etc., are equipped on vehicle or mounted on frastructure perceive vehicle and environment status. The purpose perceivg vehicle and environment status precely judge spatial and temporal relationship between two traffic objects, so decion-makg component can generate an optimal control strategy and better coordate traffic. The perceived status can be transmitted by several kds communication modes, like DSRC [12], 4G, 3G, WIFI, etc. The performance different communication modes have been deeply researched help fd appropriate communication mode applied a certa traffic scenario [13]. In CAVs based scenarios and applications simulation, accuracy perceived status and performance communication should be taken account enhance reality simulation. In th paper, framework, s and implementation CAVs kematic simulation and verification are dcussed. A four-layer framework for CAVs kematic simulation proposed. Properties tersections are defed describe road network. A target position based vehicle position updatg method designed restrict vehicles move with lks, as well as simulate behaviors lane changg and turng. Vehicle kematic s are implemented mata status vehicles. Priorities for dividual vehicle control are authorized different layers. Operatg mechanms CAVs uncertaties, which are defed as position error and communication, are implemented simulation. A simulation platform based on proposed methodology developed, and experiments are conducted prove reliability methodology.

3 Sensors 217, 17, Sensors 217, 17, Multi-Layer Simulation Framework A multi-layer framework troduced accomplh simulation. Four layers are designed: road network layer, vehicle operatg layer, uncertaties lg layer and demonstratg layer. Relationship each layer showed Figure 1. Figure 1. Framework Connected and Aunomous Vehicles (CAVs) kematic simulation. The road network layer defe properties tersection and describe road network. Intersections are are considered as as basic basic elements, elements, lks are lks generated are generated based onbased properties on properties each tersection. each tersection. Signal control Signal strategy control andstrategy sp signand control sp strategy sign control tersections strategy are tersections deployed are road deployed network layer. road network In vehiclelayer. operatg In vehicle layer, operatg vehicles are layer, generated vehicles fromare zone generated tersections, from zone and tersections, move with and rules move with vehicle followg, rules vehicle lane changg, followg, route lane selectg changg, androute or selectg vehicle kematic and or vehicle s. kematic In CAVs, vehicle s. position In CAVs, error vehicle and communication position error and communication always ext. Positiong always errorext. and Positiong communicatg error and s communicatg are implemented s error are lg implemented layer enhance error lg reality layer enhance simulation. The reality demonstratg simulation. layer The fordemonstratg vualization and layer evaluation. for vualization and evaluation. Based on CAVs kematic simulation framework, one vehicle able move from its start zone its destation zone along pre-generated route. For CAVs applications, scenarios and control strategies would be be implemented simulation, and and several several components would would modify modify status status vehicles. vehicles. Thus, Thus, priority priority vehicle vehicle control control authorized authorized different different layers layers ensure ensure stability stability simulation. simulation. A hazardous A hazardous over-speed over-speed scenario scenario and pre-warng and pre-warng control control strategy strategy would would be analyzed be analyzed as example as an example expla expla vehicle control vehicle priorities. control When priorities. one vehicle When one generated vehicle generated road network, road basic network, vehicle kematic basic vehicle s, kematic whichs, are vehicle which followg are vehicle, followg lane changg, lane, changg route selectg,, route selectg turng, etc., turng would, generate etc., vehicle would status generate stepvehicle by stepstatus leadstep vehicle by step movg lead its vehicle destation movg tersection. its destation When tersection. scenario conditions When are scenario satfied, conditions vehicle are would satfied, be controlled vehicle would by be scenario controlled component. by scenario Meanwhile, component. control Meanwhile, strategy control would monir strategy would status monir vehicle, status and generate vehicle, pre-warng and generate suggestions pre-warng suggestions control vehicle control if necessary. vehicle After if necessary. pre-warng After pre-warng relieved, relieved, authority authority control vehicle control would vehicle be given would back be given vehicle back kematic vehicle s. kematic Thus, s. control Thus, strategies, control scenarios strategies, and scenarios basic vehicle and basic kematic vehicle s kematic are s authorized are with authorized priority with high, priority medium and high, lowmedium respectively. and low Vehicle respectively. control component Vehicle control would component realize its function would by realize eir its changg function by acceleration eir changg vehicle, acceleration or alterg vehicle, or trajecry. alterg vehicle When deployg trajecry. CAV-based applications reality, usually frastructures should be built. As one When most important deployg components CAV-based applications CAV system, frastructure reality, usually apparently frastructures has a number should be functions: built. As one most important components CAV system, frastructure apparently has a number

4 Sensors 217, 17, transferrg formation, surveillg vehicle and environment status, makg decions, etc. For functions communicatg and computg, re no need set up frastructures simulation, because simulation, status every dividual unit can be obtaed and computg would be executed aumatically based on pre-designed algorithms. For roadside sensors, which are aimg surveil vehicle and environment status, y can be simulated as scenario and control strategy. Surveillance radar would be analyzed as an example. Surveillance radars are usually dtributed at high rk pots, such as road-crossg, railroad crossg, and givg formation vehicles about presence movg or still objects a certa area. To simulate function surveillance radar, firstly, operatg mechanm radar should be ed, and data type surveillg formation should be designed based on pre-extg simulation environment. Then a new programmg object should be defed mata formation collected by sensor each simulation step. The programmg object should clude position movg or still object, length detection capability, ID vehicles with detection range, etc. More attributes programmg object can be designed based on different simulation goals. Then a control strategy, which process formation collected by radar, should be designed and applied simulation. The output control strategy can be cusmized by user. It can be eir speed suggestion vehicle, or lane suggestion vehicle. Lastly, we can evaluate performance surveillance radar by analyzg movement vehicles. In th paper, properties vehicles and tersections are defed detail for vehicle control components better control vehicles, and a target position based vehicle position updatg method troduced accomplh change vehicle trajecry. 3. Defition Properties Intersections and Vehicles 3.1. Properties Intersection Intersections are basic elements composg road network. Lks can be described accordg properties tersections. In th paper, tersection properties are defed describe a road network. The properties are defed Table 1. Table 1. Defitions tersection properties. Basic Lk Name Details Fixed f slot The value equals 1 if tersection slot based tersection (SIs) Yes f sp The value equals 1 if tersection sp sign based tersection Yes f zone The value equals 1 if vehicles can be generated and ended th tersection f sign The value equals 1 if tersection signalized Yes pt Position tersection center Yes lnk n Index nth lk Yes lnk num Count lks connected th tersection Yes lnk cid n Intersection ID at or side lnk n Yes lnk l_num n Count lanes lnk n Yes lnk l_w n Lane width lnk n Yes lnk edge n Direction lnk n tersection A Yes lnk v_num n Count vehicles lnk n at a certa simulatg step No lnk len n Lk length lnk n Yes Yes

5 Sensors 217, 17, Table 1. Cont. Signalization Name Details Fixed Lane lnk left n,m The value equals 1 if lane m authorized for left turng Yes lnk str n,m The value equals 1 if lane m authorized for straight gog Yes lnk right n,m The value equals 1 if lane m authorized for right turng Yes lnk allow n,m Determed by real-time signalization and channelization. If value equals 1, vehicle allowed enter tersection with lane m lnk vhc n,m Count vehicles with lane m No lnk vhc n,m,l ID vehicle which queued lth with lane m lnk n No lnk limit n Speed limit lnk n Yes lnk cid_e n Direction lnk n lnk cid n ph n Index nth phase Yes ph num Count signal phases Yes ph north n,m ph south n,m ph east n,m ph west n,m m {, 1, 2}, and, 1, 2 represent left turng, straight gog and right turng respectively. If ph north n,m equals 1, correspondg movement authorized for vehicle comg from north. See ph north n,m See ph north n,m See ph north n,m ph time n Duration phase n Yes ph g_r n The remag green time phase n No ph Index phase beg executed No No Yes No No No No Each tersection would have correspondg properties and properties are itialed before simulation starts. After simulation begs, properties are updated each simulation step. The column that marked as fixed dicates that wher value would change each simulation step. The proposed method supports up four directions one tersection. The up edge tersection figured as north, botm edge figured as south, left edge figured as west, and right edge figured as east. In order make properties tersections easier understand, a road network with two tersections troduced expla meangs properties Figure 2. Sensors 217, 17, lnk 2 lnk l_num 2 2 ph ph ph ph num time 1 g_r s 25 s lnk edge 2 1 pt fzone 1 allow lnk2, m 1, m,1 lnk lnk lnk vhc 1,1 2 1, lnk 1 str 1, lnk 1 str right 1,1 1,1 left 1,2 1,2 len lnk 1 vhc lnk 1,1,1 vhc lnk 1,1,2 lnk 1 limit lnk1 2 m/s l_w lnk1 3.5 m lnk cid_e 1 4 cid lnk 1 lnk 3 f f f sign sp slot 1 lnk 4 allow m lnknum 4 south ph1, m 1, m 1, 2,3 lnk4, 1, m,1 vhc lnk 1,2,1 lnk v_num 1 3 Connection between two tersections can be defed Figure 2. Properties tersection. Paper uses properties tersection defe properties lk. When one tersection beg placed simulation environment, relationship tersection and pre-selected tersection would be establhed aumatically (connectg lk exts). The lks would aumatically be updated if positions tersections are changed, and connectg edge lk tersection would be updated. When simulation began, each tersection

6 Sensors 217, 17, Paper uses properties tersection defe properties lk. When one tersection beg placed simulation environment, relationship tersection and pre-selected tersection would be establhed aumatically (connectg lk exts). The lks would aumatically be updated if positions tersections are changed, and connectg edge lk tersection would be updated. When simulation began, each tersection would have a correspondg programmg object mata status tersection by updatg parameters object each simulation step. In th way, we can built enough large road network and guarantee simulation reliability Properties Vehicle The properties vehicle are designed as below Table 2. Table 2. Defitions vehicle properties. Name Details Fixed pt Present position a certa simulation step No pt en Position that vehicle movg No v Speed vehicle No st Previous tersection id No en Next tersection id No l Index lane vehicle No sta pt The value equals 1 if vehicle lk No des Destation tersection Yes r n ID nth tersection vehicle should travel through Yes r Count tersections vehicle has already passed No sta run The value equals 1 if vehicle reaches its destation tersection No sta sp The value equals 1, vehicle sps Yes d Dtance from pt pt en Yes l des Destation lane, determed by route and channelization Yes r num Count tersections cluded r n Yes vhc ahd ID vehicle ahead. The value equals 1 if no ahead vehicle exts No vhc bhd ID vehicle behd. The value equals 1 if no behd vehicle exts No sta lane The value equals 1 if vehicle changg lane No l old Previous lane No a Acceleration vehicle No vhc old Previous behd vehicle ID No sta sc The value equals 1 if vehicle volved scenarios No t re Driver s reactg time when vehicle brakes Yes vhc type Vehicle type, bus, truck or car Yes r lc Determes when change lane a lk Yes sta slot The value equals 1 if vehicle controlled by slot based algorithm No t sp Waitg time when vehicle a sp sign tersection No sta sp The value equals 1 if vehicle spped by sp sign tersection No Same with tersection properties, each vehicle would have correspondg properties. The properties would be itialed when vehicle released, and would be updated each simulation step. The column that marked as fixed dicates that wher value would change each simulation step. When one vehicle released road network from zone tersections based on Orig Destation (OD) formation, properties that conta route, lane, destation lane, itial speed, ahead vehicle, start tersection, end tersection, destation tersection, lane changg rate, etc., vehicle would be set defaults. All default values can be cusmized based on different requirements by users. The route formation calculated based on a mimum route dtance method [14], and or route selectg methods apply. Then vehicle would move a straight way wards target position. Vehicle queues each lane would be mataed rigorously apply vehicle followg. When vehicle changg lane or enterg tersection, target position would be changed simulate different vehicle behaviors. For vehicle that enterg tersection, if vehicle turng, a circular movg [15] implemented update its position, else straight position updatg method applied. And if tersection

7 Sensors 217, 17, destation vehicle, sta run vehicle would be set 1 and vehicle would not be updated or dplayed simulation. Acceleration and target position are ma attributes that are leadg vehicle. Acceleration determed by vehicle followg and crug s, and target position determed by vehicle trajecry. To better expla how vehicles are controlled by each component, Figure 3 presented below show vehicle operation process from time it generated time it fhes its journey. Sensors 217, 17, Start Kematic s Position updatg Endpt determation Simulatg one timestep Release lk sta pt 1 stapt 1 Yes No If vehicle startsection No stapt 1 Update d pt If vehicle endsection No Behd or ahead vehicle changed Yes end yes Circular movg en no des stapt yes starun 1 dtance( pt, pt ) 1 m en yes Change vhc ahd stalane 1, vhc d bhd no Yes Straight movg Yes No st en en r ( nr ) n yes stalane l des no l no yes stapt 1 Set en sp le as new end pt en Set en sp le as new end pt en d r lnk lc yes len n no Add vehicle new vhc queue lnk nml,, no set new ddd vhc ahd Lane changg criterion Speed v set new vhc ahd ddd set new vhc ahd ddd yes stalane 1 no Acceleration ad vhc bhd Set Behi -1 l old Set ol l l l l des des l l Refresh vhc status bhd ahead vehicle Set old vhc old Add vehicle lane vhc queue lnk nml,, Followg criterion r r 1 Delete from oldlane vhc queue lnk nml,, Set new d pt en Figure Vehicle operation process Matag Vehicle Kematic Status 4.1. Vehicle Speed Determation by by Followg and Crug Models Movements followg vehicle are significantly fluencedby by leadg vehicle. A A segmented queue ory applied mata sequence vehicles each lane. The segmented queues are are defed as as below Figure 4. 4.

8 4.1. Vehicle Speed Determation by Followg and Crug Models Movements followg vehicle are significantly fluenced by leadg vehicle. A segmented queue ory applied mata sequence vehicles each lane. The segmented queues are defed as below Figure 4. Sensors 217, 17, Figure 4. Segmented queue ory. Figure 4. Segmented queue ory. As shown figure, queues are defed based on lanes. When one vehicle crosses sp le tersection or change anor lane, vehicle volved anor queue and would be deleted from previous queue. In some tersections, due channelization tersection, right turng vehicles and straight gog vehicles may merge same queue. In th situation, tersection control strategy would decide which be leadg vehicle and which be followg vehicle as vehicles are prioritized enter tersection. The queue formation saved lnk vhc n,m,l and updated as simulation proceeds. Mimum safe dtance vehicle followg [16] and constant speed crug are applied determe acceleration and speed vehicles. The vehicle followg can be described as below. s gap + v2 v 2 l f τ + 2a dcc 2(a dcc /2) (1) where v l denotes leadg vehicle speed, v f denotes followg vehicle speed, a dcc = 6 m/s 2 maximum deceleration [17], s gap gap between two vehicles, τ mimum safe dtance between two vehicles with value 5 m. Vehicle speed can be updated as below: v n+1 = v n + a dcc t step /2 (s gap + v 2 l /(2a dcc ) τ + v 2 f /a dcc & v n < v limit ) v n+1 = v n (s gap + v 2 l /(2a dcc ) τ + v 2 f /a dcc & v n v limit ) v n+1 = v n a dcc t step /2 (s gap + v 2 l /(2a dcc ) < τ + v 2 f /a dcc ) (2) v n denotes vehicle speed time step n, and v n+1 denotes vehicle speed time step n + 1, v limit denotes lk speed limit, t step terval time step, usually range t step from 1 ms 1 s [18]. When one vehicle approachg tersection sp le, vehicle should sp if wayleave lane not authorized. The wayleave lane determed by attributes signal and channelization Vehicle Position Updatg Method Figure 5 shows vehicle position updatg processes straight gog and turng.

9 if wayleave lane not authorized. The wayleave lane determed by attributes signal and channelization Vehicle Position Updatg Method Sensors 217, 17, Figure 5 shows vehicle position updatg processes straight gog and turng. 3.5 x 6 = 21 m 3.5 m pt ( x, y ) n1 n1 pt (, ) xn yn 3.5 m pt (, ) en x y Next tersection Green belt r n1 n1 position updatg lk position updatg tersection pt ( x, y ) 3.5 m 3.5 m 3.5 m 3.5 m ( x, y ) r r pt (, ) xn yn 3.5 m Figure 5. Position updatg. Figure 5. Position updatg. (x n, y n ) denotes position vehicle at simulation step n, (x, y ) denotes pt en at simulation step n, (x n+1, y n+1 ) denotes position vehicle at simulation step n + 1, r denotes vehicle turng radius which can be calculated with properties tersection, (x r, y r ) denotes center turng circle, (x n+1, y n+1 ) calculated with criteria vehicle kematic s and trajecries. For turng vehicles, (x n+1, y n+1 ) can be calculated by formula below. θ = t step v/r + arcs((y n y r )/r) x n+1 = x r + r cos θ y n+1 = y r + r s θ (3) For straight gog vehicles, (x n+1, y n+1 ) can be calculated by formula below. { (xn+1 x n )/(y n+1 y n ) = (x x n )/(y y n ) (x n+1 x n ) 2 + (y n+1 y n ) 2 = ( v t step ) 2 ( y n < x n+1 < x y n < y n+1 < y ) (4) As showed formula, updatg position turng vehicle does not rely on pt en but based on properties tersection stead. However, straight gog vehicle position updatg should relate pt en. A target position matag method proposed lead vehicle move along its trajecry Target Position Updatg There are two situations that target position vehicle should be modified. One when vehicle changg lane, and or when vehicle enterg tersection. The equation below shows a mimum safety dtance based vehicle lane changg criterion implemented platform. s lon s f s l + L + W s θ (5) where s lon longitudal gap between lane changg vehicle and its behd vehicle target lane, s f longitudal dtance behd vehicle has moved durg lane changg duration t c, s l longitudal dtance lane changg vehicle has moved durg lane changg duration t c. In th paper, t c set 3s [19]. L length vehicle, W width vehicle, θ denotes angle between lane and direction that lane changg vehicle moves. When

10 As shown figure, vehicle operatg layer, vehicles are movg accordg vehicle kematic s. In uncertaties lg layer, observed positions vehicles are changed due overlapped position error and communication. The demonstratg layer for dplayg and dplay vehicles and road network can be scaled and translated. Lateral position error maly effects lane identification vehicle. Now th target lane, s f longitudal dtance behd vehicle has moved durg lane changg sl longitudal dtance lane changg vehicle has moved durg lane t duration c, t t changg duration c. In th paper, c set 3s [19]. L length vehicle, W Sensors 217, 17, width vehicle, denotes angle between lane and direction that lane changg r vehicle attributemoves r lc and. When mimum attribute safety dtance lc and based lane mimum changg safety criterion dtance are satfied, based lane vehicle changg starts criterion changeare lane, satfied, target vehicle position starts can change be calculated lane, Figure target position 6. can be calculated Figure 6. Lane changg time t c Next step pt ( xn 1, yn 1) Present trajecry ( ) en x 1, y1 After lane changg be calculated pt ( x, y ) en pt ( xn, yn) Sensors 217, 17, Figure 6. Target position redefed when vehicle changg lane. By solvg equations below, target position can be obtaed. By solvg equations symsbelow, x ( x,x target ), y position (, ) can be obtaed. 1 n ( x1 xn) syms +( y1 x 1 y n) (x =( n v, nx t c), ) y 1 (, ) { (x1 n ) 2 (6) ( y + (y y n ) 2 = (v t c ) 2 yn) x1( x xn) y1xyn yxn (6) (y y n )x 1 (x x n )y 1 +x y n y x n w 2 2 l sqrt(( y y = w sqrt((y n) ( x x y n ) 2 +(x n) x n ) 2 l When one vehicle fhes lane changg movement, or enters tersection, new target When one vehicle fhes lane changg movement, or enters tersection, new target position would be set middle sp le present lane. Except for lane changg position would be set middle sp le present lane. Except for lane changg triggerg condition, target position should be updated when dtance between vehicle triggerg condition, target position should be updated when dtance between vehicle and and target position smaller than 1 m. target position smaller than 1 m. 5. Uncertaties Operatg Mechanms Qualities communication and positiong would significantly fluence effectiveness applied algorithms and control strategies. Position Position error error and communication s and operatg mechanms mechanms are are analyzed analyzed th th paper. paper. Figure Figure 7 shows 7 shows observg observg position position vehicle that vehicle fluenced that fluenced by uncertaties by uncertaties vehicle vehicle operatg operatg layer, uncertaties layer, uncertaties lg lg layer andlayer demonstratg and demonstratg layer. layer. Communication Positiong error Prece Figure 7. Observg positions vehicles fluenced by uncertaties. Figure 7. Observg positions vehicles fluenced by uncertaties.

11 Sensors 217, 17, As shown figure, vehicle operatg layer, vehicles are movg accordg vehicle kematic s. In uncertaties lg layer, observed positions vehicles are changed due overlapped position error and communication. The demonstratg layer for dplayg and dplay vehicles and road network can be scaled and translated. Lateral position error maly effects lane identification vehicle. Now th problem can be well resolved by real-time video processg technology with accuracy 97% [2]. In th case, only longitudal position error dcussed. Communication would fluence punctuality formation transmittg. Frequent formation teractg would significantly fluence performance system. A CAV system contas variety communication teractg types: teraction with one sgle device, transmsion between two different equipment, multi-hop transmsion among several components. Different communication procols would lead different communication s. In th paper, sgle-hop transmsion dcussed as an example for lg communication. Or communication s can be applied by replace proposed Sensors 217, 17, th paper. Longitudal position error and communication would affect observg dtance would between affect two vehicles. observg Indtance th case, between it may lead two vehicles. false pre-warng In th case, or it pre-warng may lead false failure prewarng no filterg or algorithms pre-warng are failure taken. if no filterg algorithms are if taken Gaussian Gaussian Dtribution Dtribution Based Based Position Position Error Error Model Model It It difficult difficult establh establh an an accurate accurate error error for for position position error; error; adays, adays, most most extg extg methods methods are are based based on on Gaussian Gaussian dtribution. dtribution. Lee Lee [21] [21] defed defed Gaussian Gaussian dtribution-based dtribution-based trajecry trajecry s s design design threshold threshold parameters parameters for for a rear-end rear-end collion collion avoidance avoidance system. system. Although Although some some researchers researchers use use or or methods methods GPS GPS positiong positiong error error as as a practical practical alternative alternative [22], [22], we we still still propose propose mostly mostly adopted adopted boundg boundg method method (Gaussian (Gaussian dtribution dtribution function) function) represent represent dtribution position error ϕ. dtribution position error. ϕ ( F(ϕ) = 2πσ 2 e (y µ)2 y) /2 /2σ F( ) e dy 2 dy (7) (7) 2 2 To determe value σ and and µ above above equation, equation, an experiment an experiment has has been been conducted conducted Shanghai, Cha. Cha. AA car car equipped with three kds positiong systems are driven on express way generate positiong data. The three kds positiong systems are: INS (Inertial Navigation System) with RTK (Real Time Kematic) positiong (th system considered as most prece system experiment, or two systems are compared with th one), RTK positiong system and RTD (Real Time Differential) positiong system. The trajecry experiment showed Figures 8 and 9. Figure 8. Vehicle trajecry experiment. ude

12 Sensors 217, 17, latitude Figure 8. Vehicle trajecry experiment. Sensors 217, 17, Figure Figure Data Data map map and and pot pot set. set. Positiong data collected once per second each positiong system. Invalid pot sets (at a certa time, one or more positiong systems did not generate valid position data) are deleted ( Positiong data collected once per each positiong system. Invalid pot setspot (at a dcontuity trajecry because second th) from data sheet. Total number valid certa time,position one or more did not generate position data) are deleted sets error positiong defed assystems dtance between INSvalid position and RTK/RTD position.( So dcontuity trajecry because th) from data sheet. Total number valid pot we can get position error matrix ek and ed. ek denotes error RTK positiong system, sets Position error defed as dtance between INS position and RTK/RTD position. ek and ed error and RTD system. are both 1 positiong 4728 matrix. With and So weedcandenotes get position errorpositiong matrix ek and ed. ek denotes RTK system, ed denotesekand RTDepositiong system. e and ed areboth 4728 matrix. WithConsiderg defition ek 1and ed are defition positive. that d, we can see that all k values ek and ed,value we can see that all values approximates ek and ed are positive. Considerg that mean value mean positiong error should and symmetry position error, we positiong error should approximates and symmetry position error, we establh new establh new position error matrix [ ek ek ] and [ ed ed ] for analyzg. The densities RTK position error matrix [ ek ek ] and [ ed ed ] for analyzg. The densities RTK and RTD position error and RTD position error are showed Figure 1.are showed Figure RTK RTK fittg.2 RTD RTD fittg error (m) (a) RTK error, fittg curve 5 = 2.17, error (m) (b) RTD error, fittg curve 5 = 4.37, 1 15 Figure 1. Density RTK and RTD position error: (a) density RTK positiong error and its Figure 1. Density RTK and RTD position error: (a) density RTK positiong error and its fittg curve; and (b) density RTD positiong error and its fittg curve. fittg curve; and (b) density RTD positiong error and its fittg curve. The position position error error dtribution dtribution subject Gaussian dtribution. RTK RTK positiong positiong system system The more prece than RTK position error error density fittgfittg curve. more than RTD RTDpositiong positiongsystem, system,sosoσ smaller RTK position density smaller In fact,in σ differs different systems and different Dion [23] has establhed curve. fact, differs positiong different positiong systems andenvironments. different environments. Dion [23] has a position error error with σ equals 2.8. th paper, usepaper, RTD. error establhed a position with equals 2.8. we In th weerror use RTDPosition error. In complyg with dtribution above superposed onsuperposed each vehicle. Position error complyg with dtribution above on each vehicle Uniform Uniform and and Rayleigh Rayleigh Dtribution-Based Dtribution-Based Communication Communication Delay Delay Model Model 5.2. Due different different system system structures structures and and formation formation retransmsion retransmsion mechanms, mechanms, communication communication Due difficult evaluate. For V2V communication, DSRC widely used. In th paper, DSRC-based difficult evaluate. For V2V communication, DSRC widely used. In th paper, DSRCsgle-hop V2V communication dcussed. The communication generatg can based sgle-hop V2V communication dcussed. The communication process generatg be described Figure 11. Figure 11. process can bedescribed Generally, positiong devices output vehicle position data 1 times per second. The positiong time followg vehicle and leadg vehicle cannot be accurately synchronized. Th kd asynchronization leads communication, and formation transmsion also results d communication. For part communication 1 caused by asynchronization

13 Sensors 217, 17, Generally, positiong devices output vehicle position data 1 times per second. The positiong time followg vehicle and leadg vehicle cannot be accurately synchronized. Th kd asynchronization leads communication, and formation transmsion also results communication. For part communication d 1 caused by asynchronization positiong time, uniform dtribution applied describe it. The dtribution function can be defed as below. F 1 (d 1 ) = Sensors 217, 17, d 1 1 dy (8) 1 Followg Vehicle (FV) Leadg Vehicle (LV) FV position LV position LV position Positiong out sync Transmit FV position LV position LV position Positiong out sync Transmit Figure 11. Communication generatg process. The The transmsion transmsion establhed establhed based based on on data data paper paper [23]. [23]. After After analyzg analyzg data data fiducial fiducial probability probability.5,.5, we we found found that that data data subject subject Rayleigh Rayleigh dtribution dtribution with with mean mean value value equals equals ms. ms. Probability Probability density density function function Rayleigh Rayleigh dtribution dtribution defed defed as as below. below. x f2( x) e x /2 f 2 (x) = x (9) σ 2 e x2 /2σ 2 (9) And due characters Rayleigh dtribution, mean value defed as below. E(D 2E(D ) 2 ) = σ / 2π/ σ = (1) (1) Then Then we we can can get get value value σ = With With Equation Equation (9), (9), we we can can get get transmsion transmsion as as below. below. d d 2 2 y y y 2 / F2( df 2 (d ) 2 ) = e e y2 / dy dy (11) (11) The communication sum asynchronization and transmsion. The communication sum asynchronization and transmsion. To To implement communication simulation, an array with 4 rows has been defed implement communication simulation, an array with 4 rows has been defed computer memory. The communication simulatg process showed Figure 12. computer memory. The communication simulatg process showed Figure 12. Vehicle formation would be saved array step by step. An dex used determe which item should be used be dplayed or be applied applications. For or communication Vehicle formation modes, communication s are able be applied simulation with proposed framework. Status with Adrress at time t Status with at time t +t step Adrress 1 = m*t step Real-time status at time t Real-time status at time t +t step Adrress m-2 Adrress m-1 Adrress m Adrress m+1

14 y y 2 / F2( d2) e dy (11) The communication sum asynchronization and transmsion. To implement communication simulation, an array with 4 rows has been defed Sensors 217, 17, computer memory. The communication simulatg process showed Figure 12. Vehicle formation Status with at time t Status with at time t+tstep Adrress Adrress 1 = m*t step Real-time status at time t Real-time status at time t+tstep Adrress m-2 Adrress m-1 Adrress m Adrress m+1 Sensors 217, 17, 1938 Adrress communication modes, Figure communication 12. Communication s simulatg are able process. be applied simulation with proposed framework. 6. Simulation Vehicle and formation Verification would be saved array step by step. An dex used determe 6. Simulation and Verification which item should be used be dplayed or be applied applications. For or Besides Besides proposed proposed simulation simulation method, method, a user a user terface terface with with a dozen a dozen functions, functions, which which clude establhg clude road establhg network, road settg network, parameters settg parameters put, settg put, consecutive settg consecutive simulation simulation or sgle step, or sgle road step network, road savg, network illustratg savg, illustratg simulation with simulation graphics with device graphics terface device (GDI) or GDI+, terface scalg (GDI) and or translatg GDI+, scalg dplay, and translatg etc., designed dplay, enhance etc., designed simulation. enhance The simulation platform simulation. showed The simulation Figure 13. platform showed Figure 13. Network OD Vehicle status Channelization and Signal Figure Simulation platform. The comparon lk traditional road network, fluence uncertaties prewarng algorithms and a CAVs application are conducted platform verify applicability and reliability platform Vehicle Delay Comparon with Traditional Road Network Scenarios and applications CAVs are all based on traditional traffic flows. That wher

15 Sensors 217, 17, The comparon lk traditional road network, fluence uncertaties pre-warng algorithms and a CAVs application are conducted platform verify applicability and reliability platform Vehicle Delay Comparon with Traditional Road Network Sensors Scenarios 217, 17, 1938 and applications CAVs are all based on traditional traffic flows. That wher basic traffic flow cocides with real traffic flow would significantly fluence results condition any control strategies fixed signal and control algorithms test verified and verify reality platform. Vehicle platform. average Volume capacity analyzed ratio condition troduced fixed signal verification. control The test volume and verify capacity reality ratio defed platform. as below. Volume capacity ratio troduced verification. The volume capacity ratio defed as below. η = ϕ (12) g/c e (12) φg e /C where denotes vehicle arrival flow rate, denotes saturation flow rate, C denotes where ϕ denotes vehicle arrival flow rate, φ denotes saturation flow rate, C denotes traffic traffic signal cycle signal length, cycle length, g e denotes g e denotes effective effective green terval green duration, terval duration, and η denotes and denotes volume volume capacity ratio. capacity Theratio. saturation The saturation flow rate flow valuerate equals value equals 18 vhc/h 18 [24]. vhc/h The [24]. saturation The saturation flow rate flow rate defed defed Figure Figure Lost time Lost time Vehicle flow rate Red Saturation flow rate Green Figure 14. Determtic queug analys. Due 1994 HCM estimate [25], a one-lane-one-tersection scenario set up and simulated for 15 m estimate. The scenario showed Figure Amber Red Simulation parameters Simulation time: 15 m Saturation flow: 18 veh/h Free-flow speed: 6 km/h Signal operation Cycle time: 6 s Red terval : 3 s Green terval: 3 s 2km 1km Figure 15. Delay evaluation scenario. Thanks vehicle kematic s implemented platform, saturation flow approximately 18 vhc/hat at signal tersection. Figure16 16 shows results every vehicle and vehicle average different volume capacity ratio.

16 Figure 15. Delay evaluation scenario. Thanks vehicle kematic s implemented platform, saturation flow approximately 18 vhc/h at signal tersection. Figure 16 shows results every vehicle and vehicle average different volume capacity ratio. Sensors 217, 17, Sensors 217, 17, Sensors 217, 17, Figure 16. Vehicle different volume capacity ratio: (a d) are vehicle when different volume capacity ratio: (a d) are vehicle when equals Figure 16..6, Vehicle.9, 1. and 1.1 different respectively. volume capacity ratio: (a d) are vehicle when η equals equals.6,.9,.6, 1..9, and1. 1.1and respectively. 1.1 respectively. Delay data calculated and saved when one vehicle reached its destation. Sgle vehicle vibrates Delay because data some calculated and vehicles savedare when fluenced one vehicle by reached red its its light destation. while some Sgle vehicles vehicle are not. vibrates because some vehicles are are by red are not. with fluenced by red light while some vehicles are not. Average vehicle res along with. When 1 Average vehicle res along with η. When. When η 1, sgle 1, sgle vehicle value remas a, sgle vehicle vehicle value value remas remas a certa a certa range and when 1 sgle vehicle rg along with time, th condition reflects certa range and range when and η when > 1 sgle 1 sgle vehicle vehicle rg rg along along with with time, time, th th condition condition reflects reflects defition defition defition volume volume volume capacity capacity capacity ration. ration. ration. Average Average Average different different different η simulated simulated simulated and and and showed showed showed Figure Figure Figure HCM value HCM value fittg curve simulatg HCM value fittg curve simulatg value fittg curve simulatg fittg curve Figure 17. Average different conditions. Figure 17. Average different conditions. Compared results from Dion [24], results simulated by platform correspond Compared results from Dion [24], results simulated by platform correspond results calculated by HCM, which dicate that platform reliable. results calculated by HCM, which dicate that platform reliable Verification Uncertaties 6.2. Verification Uncertaties Pre-warngs are adopted analyze effect uncertaties s implemented platform. Pre-warngs Hazardous are adopted vehicle maneuver analyze over effect speed generated uncertaties as basic s scenario, implemented and brakg platform. dtance based Hazardous pre-warng vehicle method maneuver with maximum over speed deceleration generated 6 as m/s basic 2 [26] scenario, embedded and brakg as dtance control strategy. based pre-warng The aforementioned method with Gaussian maximum dtribution deceleration based position 6 m/s error 2 [26] embedded with standard as control deviation strategy The and aforementioned average value Gaussian, and Rayleigh dtribution dtribution based position based DSRC error communication with standard deviation with 4.37 standard and average deviation value 23.93, and and Rayleigh average dtribution value based are DSRC applied communication simulation uncertaties. with standard The speed deviation followg and vehicle average 3 m/s value and speed are applied leadg simulation vehicle 2 uncertaties. m/s. The scenario The was speed executed followg 1 times vehicle and results 3 m/s are and showed speed Figure 18. leadg vehicle 2 m/s. The scenario was executed 1 times and results are showed Figure 18.

17 Sensors 217, 17, deviation 4.37 and average value, and Rayleigh dtribution based DSRC communication Sensors 217, 17, with standard deviation and average value are applied simulation uncertaties. The speed followg vehicle 3 m/s and speed leadg vehicle 2 Sensors m/s. 35 The 217, scenario 17, 1938 was executed 1 times and results 14 are showed Figure test value mimum gap 8.33 m normal condition 12.5 m test value mimum gap 8.33 m normal condition 12.5 m times (a) position error test value mimum gap 8.33 m normal condition 12.5 m test value mimum gap 8.33 m normal condition 12.5 m times 8 (b) communication times times Figure 18. Verification (a) position uncertaties: error (a) gap between two vehicles (b) when communication applyg Gaussian dtribution positiong error ; (b) gap between two vehicles when applyg uniform and Rayleigh Figure 18. dtribution Verificationcommunication uncertaties: (a) gap between two vehicles when applyg Gaussian dtribution positiong error error ; (b) (b) gap between two vehicles when applyg uniform and In Rayleigh figure, dtribution Y-ax communication denotes dtance. between followg vehicle and leadg vehicle when pre-warng generated. The results test value le are based on test uncertaties. In figure, The mimum Y-ax denotes gap le based dtance dtance on between between condition followg that followg vehicle deceleration vehicle and value and leadg warng leadg vehicle method vehicle when when equals pre-warng 6 pre-warng m/s 2 and generated. normal generated. The condition results The test le results value based le test on are value based condition le are that based test on uncertaties. deceleration test value uncertaties. The mimum warng The gapmethod mimum le equals based gap le on4 m/s based condition 2. In position that condition error deceleration and that communication deceleration value warng value simulation, warng method method equals results equals 6fluctuate m/s 2 and 6 m/s around 2 and normal oretical normal condition condition le result. based The le dtribution based condition condition that results corresponds deceleration that deceleration with value Gaussian value warng method warng dtribution equals method equals 4 m/s and 2. Rayleigh In4 position m/s 2. dtribution In error position and error communication. and The communication results proved simulation, reliability simulation, results fluctuate results platform. around fluctuate around oretical oretical result. Theresult. dtribution The dtribution results corresponds results corresponds with Gaussian with Gaussian dtribution dtribution and Rayleigh and dtribution Rayleigh dtribution. The. results proved The results reliability proved reliability platform platform. Verification CAVs Application: Slot-Based Intersections 6.3. Verification CAVs Application: Slot-Based Intersections Tachet [27] proposed a slot-based tersections (SIs) control strategy CAVs. The concept SIs 6.3. Verification Tachet [27] proposed CAVs Application: a slot-based Slot-Based tersections Intersections enlarge dtance between two vehicles deliberately (SIs) control so that strategy conflictg CAVs. The vehicle concept can cross SIs tersection Tachet enlarge[27] with proposed dtance between a gap slot-based between twotersections vehicles deliberately vehicles. (SIs) control The sosis that strategy control conflictg strategy CAVs. The vehicle implemented concept can cross SIs simulation tersection enlarge platform with dtance and between gap between control two vehicles strategy twodeliberately vehicles. realized The between so SIs that control demonstratg conflictg strategy vehicle implemented layer can and cross uncertaties tersection simulationg platform with layer. andgap The between control tersection strategy two properties vehicles. realizedthe are between SIs control same with demonstratg strategy Figure 5. implemented Simulation layer and put uncertaties simulation 36 vhc/h g platform each layer. and lane The has control tersection been strategy conducted properties realized and are between average same with demonstratg Figure vehicles 5. Simulation layer showed and uncertaties put Figure vhc/h g each layer. lanethe hastersection been conducted properties and are average same with Figure vehicles 5. Simulation showed put Figure vhc/h each lane has been conducted and average vehicles showed Figure 19. average (s) average (s) Figure Figure Average Average each each lk lk S. S. comg direction comg direction The comg directions are Figure defed 19. Average properties each tersection. lk S. Furrmore, based on control strategy, simulation with put 55, 65, 75 and 85 vhc/h per each lane has been conducted The comg estimate directions bottleneck are defed method. properties The results tersection. are showed Furrmore, Figure 2. based on control strategy, simulation with put 55, 65, 75 and 85 vhc/h per each lane has been conducted estimate bottleneck method. The results are showed Figure 2.

18 Sensors 217, 17, The comg directions are defed properties tersection. Furrmore, based on control strategy, simulation with put 55, 65, 75 and 85 vhc/h per each lane has been conducted Sensors estimate 217, 17, 1938 bottleneck method. The results are showed Figure average (s) average (s) average (s) average (s) Figure 2. Average different puts: (a d) are average vehicle when put each lane 55, 65, 75 and 85 vhc/h respectively. The The bottleneck bottleneck SIs SIs control control strategy strategy does does ext, ext, and and value value between between and and vhc/h vhc/h per per lane. lane. The The simulation simulation and and results results dicate dicate that that simulation simulation platform platform able able support support simulation simulation and and verification verification CAVs CAVs applications. applications. And And three three different different tersection tersection control control strategies strategies that that are are signal signal control, control, sp sp sign sign control control and and SIs SIs control, control, are are compared compared platform. platform. And And simulation simulation results results are are constent constent with with Tachet Tachet [27]. [27]. The The results results are are showed showed Figure Figure SIs control fixed signal sp sign time (s) Figure 21. Comparon among different control strategies. 7. Conclusions A four-layer framework, which composed with road network layer, vehicle operatg layer, uncertaties lg layer and demonstratg layer, for CAVs kematic simulation proposed th paper. By conductg verification comparon lk traditional road network, fluence uncertaties pre-warng algorithms and a CAVs application, methodology proposed th paper turns out be reliable. Th paper proposed a method how establh basic CAVs kematic simulation environment. Framework and s are dcussed. The correlations different vehicle kematic s, vehicle trajecry and road network are establhed. Based on basic CAVs simulation environment, highly specialized scenarios and control strategies related CAVs are able be realized precely. Due four layer framework

19 Sensors 217, 17, proposed th paper turns out be reliable. Th paper proposed a method how establh basic CAVs kematic simulation environment. Framework and s are dcussed. The correlations different vehicle kematic s, vehicle trajecry and road network are establhed. Based on basic CAVs simulation environment, highly specialized scenarios and control strategies related CAVs are able be realized precely. Due four layer framework methodology, s are easy be replaced or updated as long as outputs new s are subject defitions tersections and vehicles. In proposed method, number maximum directions an tersection designed be four. Although a four-legged tersection can cover most realtic situations, tersection attributes still should be updated volve more tersection charactertics. For different tersections, -tersection trajecry vehicle differs. The vehicle position updatg method not prece enough describe movement vehicles, especially situation when vehicle turns. Infrastructures are not ed and simulated paper. Although frastructure can be simulated by separatg its functions scenario and control strategy, th not an optimal solution. Infrastructure should be considered as an embedded component simulation framework. Basic operatg mechanm frastructure should be designed, and basic attributes frastructure should be mataed. Th work proposed a method build basic CAV kematic simulation environment. The aim work help verify CAV based applications, so performance simulation would directly fluence time-consumg verification. The simulation platform th paper establhed based on basic programmg technology. The simulation would not be smooth enough when road network and traffic large. Stware engeerg computg, dplay based on GDI and synchronization between m should be deeply researched. More uncertaties s should be implemented uncertaties lg layer. Ackledgments: We would like thank reviewers for very helpful comments. Th paper supported by National Key Research and Development Program Cha (216YFB121), State Natural Science Foundation Monumental Projects (614975), Fundamental Research Funds for Central Universities Cha (216YJS35), National Natural Science Foundation Cha ( ) and Beijg Natural Science Foundation (417249). Author Contributions: Baigen Cai and Huashen Wang conceived and designed framework simulation platform; Wei ShangGuan and Jian Wang analyzed data and optimized vehicle kematic s; Lguo Chai wrote paper. Conflicts Interest: The authors declare no conflict terest. References 1. Ratrout, N.T.; Rahman, S.M. A comparative analys currently used microscopic and macroscopic traffic simulation stware. Arabian J. Sci. Eng. 29, 34, Garg, A.; Vijayaraghavan, V.; Zhang, J.; Li, S.; Liang, X. Design robust battery capacity for electric vehicle by corporation uncertaties. Int. J. Energy Res. 217, 41, [CrossRef] 3. Garg, A.; Chen, F.; Jian, Z. State---art designs studies for batteries packs electric vehicles. In Proceedgs IET International Conference on Intelligent and Connected Vehicles (ICV 216), Stevenage, UK, September 216; IET: Stevenage, UK. 4. Mahmassani, H.S. Aunomous vehicles and connected vehicle systems: Flow and operations considerations. Transp. Sci. 216, 5, [CrossRef] 5. Gueriau, M.; Billot, R.; El Faouzi, N.; Monteil, J.; Armetta, F.; Hassas, S. How assess benefits connected vehicles? A simulation framework for design cooperative traffic management strategies. Transp. Res. Part C Emerg. Technol. 216, 67, Zha, L.; Zhang, Y.; Songchitruksa, P.; Middlen, D.R. An tegrated dilemma zone protection system usg connected vehicle technology. IEEE Trans. Intell. Transp. Syst. 216, 17, [CrossRef] 7. Fredette, D.; Pavlich, C.; Ozguner, U. Development a UDDS-Comparable framework for assessment connected and aumated vehicle fuel savg techniques. IFAC PapersOnLe 215, 48, [CrossRef]

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